U.S. telecommunication infrastructure is estimated to consume 60 billion kilowatt hours of power per year. Such an enormous consumption partially results from the fact that most networks are engineered to handle peak traffic. Network devices such as routers and switches tend to operate at full speed and consume maximum power, while typical traffic levels are only a small fraction of the maximum throughput.
One known approach to reducing energy consumption in a network involves powering down particular network devices from time to time. For example, these network devices may be placed into a sleep mode, an off state or other type of inactive state in which power consumption of the network device is considerably reduced relative to its maximum power consumption. However, during such downtime any packets arriving at the network device for processing have to be buffered, and this can cause significant delay in the transport of the packets through the network. Thus, minimizing the period of time that the network devices are in their respective active states and minimizing delays in packet transmission through the network become two conflicting goals. This problem is compounded by the fact that there is often a considerable transition time involved in switching a given network device between its active and inactive states.
In order to address the costs associated with transition of network devices between their active and inactive states, it has been proposed that edge routers of a network group packets having the same source and destination and transmit them in bursts, in order to reduce the number of transitions and increase the inactive time of the network devices. See S. Nedevschi et al., “Reducing Network Energy Consumption via Sleeping and Rate-Adaptation,” in J. Crowcroft and M. Dahlin, eds., NSDI, pp. 323-336, USENIX Association, 2008. However, such an approach can still lead to considerable delay for packet transmission through the network, and fails to provide a global optimization that simultaneously addresses both energy consumption and delay minimization.